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Big Data in Marketing; 5 Use Cases
December 4, 2017 News AI analytics big data

 

Big data is more than just a buzzword. In fact, the huge amounts of data that we’re gathering could well change all areas of our life, from improving healthcare outcomes to helping to manage traffic levels in metropolitan areas and, of course, making our marketing campaigns far more powerful.

That’s because marketers are increasingly using artificial intelligence and machine learning to parse huge amounts of data and to draw conclusions. They can even use predictive analytics to figure out what customers and prospects are likely to do in the future and to adapt their communication materials as a result of it.

In the same way that Netflix is able to use its huge amount of user data to create more personalized recommendations to its users, marketers will be able to gain a greater understanding of what people are actually doing on their websites. It makes personalization easier than ever before, and we all know how important personalization is if we want to build genuine connections with our customers.

Here are just five of the main use cases for big data in marketing.

1. More targeted advertising

As publishers gather more and more data about their visitors, it’ll enable them to serve up more and more relevant advertising. In the same way that Google and Facebook already offer up detailed targeting options, third-party vendors will offer the same array of choice. Imagine being able to target people based on the articles that they’ve read or based on a lookalike audience of your ideal reader.

The Weather Channel has already given us a glimpse of the future of advertising by analyzing the behavior patterns of its digital and mobile users in over three million locations across the globe. It combines this with its climate data to give advertisers the opportunity to send super-targeted advertisements. For example, shampoo brands can target people in humid climates with anti-frizz products.

2. Semantic search

Semantic search is the process of searching in natural language terms instead of in the short burst of keywords that we’re more used to. Big data and machine learning make it easier for search engines to fully understand what a user is searching for, and smart marketers are beginning to incorporate this into their site search functionality to improve the user experience for their visitors.

One example of this comes to us via Walmart, which uses text analysis, machine learning and synonym mining to improve the accuracy of their site search. According to Walmart, adding semantic search to their website has increased the conversion rate by 10-15%. For a company like Walmart, that adds up to millions (if not billions) of dollars.

3. More relevant content

In the same way that Netflix can serve up personalized recommendations, publishers will be able to serve up more relevant content to their visitors by tapping into their wealth of data to determine which content people are most likely to enjoy. Even content marketers will be able to get into the job, and digital marketers will need to learn to stop thinking of their blog as a static site. In the same way that you get different results when you Google the same phrase in different locations, your blog should look different depending upon who’s looking at it.

This poses a technical challenge, of course, but what else is new? The field of digital marketing is full of new challenges and it moves so quickly that those who don’t rise to the challenge will quickly be left behind. Either way, consumers will make decisions on your behalf by deciding where to click and what to purchase. And the companies that cater to those consumers by providing more relevant content will be the ones that come out on top.

4. More conclusive testing

The fact that we’re able to gather and analyze data in huge amounts will enable us to conduct much more conclusive testing because instead of just testing variants of a single factor, the algorithms of the future will enable us to factor in all sorts of additional data including visitors’ prior histories to give us more accurate – and more conclusive – test results.

For example, variant A of your landing page might work best amongst a younger crowd while variant B works better with an older crowd. Marketers will be able to use insights like these to customize their sites to make them as appealing as possible to different target audiences, serving up different variants by default depending upon what previous data suggests the user is most likely to engage with.

5. Machine-powered analytics

I’m not saying that artificial intelligence and big data will spell out the death of the human analyst, but it’s certainly true that if marketers want to draw conclusions from a huge pool of data, they’ll need the support of a machine to help to process it.

Because of this, the digital marketers of the future will need to work in tandem with machines to analyze data and to make decisions based upon it. No matter how much technology evolves, there will always be the need for a human to oversee it – and that’s even truer when it comes to the complicated field of big data analytics. No human could do it alone, and neither could any single piece of software. The combination of the two will be far more powerful than just the sum of its parts.

What’s next?

As AI and machine learning technologies get better and better, with more and more processing power and more and more data available for them to learn from, it can only get more and more important over time.

In the meantime, there’s still plenty of potential for marketers to take advantage of big data in the here and now, and we’re likely to see a future in which each company’s big data repository is just as important to their marketing efforts as their email list. It’ll be an owned asset that sets one company apart from another.

As with most things, it’ll be the early adopters who have the most to gain, because it’ll give them a head start on the competition. At the very least, it’s a good idea to evaluate your current data collection processes and to figure out what you can do now to future-proof your data.

This article was originally published on www.inc.com and can be viewed in full

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